This paper pays attention to the nearest keyword (NK) problem on probabilistic XML data (NK-P). NK search occupies an important position in information discovery, information extraction and many other areas. Compared with traditional XML data, it is more expensive to answer NK-P search because of so many possible worlds. NK-P can be seen as an NK problem on many traditional XML documents. For a given node q and a keyword k, an NK-P query returns the node which is nearest to q among all the nodes associated with k in all the possible worlds. NK-P search is not only useful independent operator but also as an important part for keyword search. Firstly, we propose a new NK concept on probabilistic XML data based on possible worlds. Next, we present an indexing algorithm to answer an NK-P query efficiently. Finally, extensive experimental results show that our approach is an effective method on probabilistic XML data, and it could significantly reduce the execution time. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Zhao, Y., Yuan, Y., & Wang, G. (2014). Nearest keyword search on probabilistic XML data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8709 LNCS, pp. 485–493). Springer Verlag. https://doi.org/10.1007/978-3-319-11116-2_43
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